Abstract: List of action verbs. Download the complete list of English action verbs writers use to enhance their readability. Ideal for hypnotic writing. Get list of verbs beginning with each letter of the alphabet: list of action verbs starting with A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S, T, U, V, W, X, Y, and Z. Download the FREE list of actions verbs, sorted alphabetically.

Abstract: List of work words or English verbs. Download the complete list of work words writers use to enhance their readability. Ideal for hypnotic writing. When you want to write any document setting forth requirements, like the scope of work (SOW) for your request for proposals (RFP), you must define the specific work to be accomplished in clear, concise language.

Abstract: A security vulnerability assessment service is a risk management process. Interliant's security vulnerability assessment service enables its clients to understand what risks their online transaction systems and network infrastructure face. Relevant Technologies has taken an in-depth look at Interliant's security vulnerability assessment service to find out what their tactical strategy is in helping organizations minimize risk, how this strategy evolved, and what IT decision makers can expect to gain from using their services.

Abstract: Data leakage and data breach are two disparate problems requiring different solutions. Data leakage prevention (DLP) monitors and prevents content from leaving a company via e-mail or Web applications. Database activity monitoring (DAM) is a data center technology that monitors how stored data is accessed. Learn why DAM complements DPL, and how you can benefit by making it part of your overall data security strategy.

Abstract: Without data that is reliable, accurate, and updated, organizations can’t confidently distribute that data across the enterprise, leading to bad business decisions. Faulty data also hinders the successful integration of data from a variety of data sources. But with a sound data quality methodology in place, you can integrate data while improving its quality and facilitate a master data management application—at low cost.

Abstract: Nearly half of all US companies have serious data quality issues. The problem is that most are not thinking about their business data as being valuable. But in reality data has become—in some cases—just as valuable as inventory. The solution to most organizational data challenges today is to combine a strong data quality program with a master data management (MDM) program, helping businesses leverage data as an asset.

Abstract: The Dale-Chall list contains 3,000 simple, familiar words, which 80% of 4th graders can understand. The list is used by the Dale-Chall Readability Grade Score (RGS) to assess the readability of written materials by rating text on a U.S. grade-school level. It is also used by other readability statistics, like the Bormuth Grade Level formula

Abstract: You can blame your sales people all you want, but if the lead data is bad, they’re not going to bring in business. You can blame your product managers for ineffective promotions, but if the target lists are redundant, the pitches fall on deaf ears. You can blame your customer service representatives for low satisfaction scores, but if customer data is missing, then no wonder the complaint resolution pipeline is backed up. Think it’s your customer resource management (CRM) system? Think again. It’s bad data, and it’s costing you millions. Request your copy of The Bottom Line on Bad Customer Data that delivers detailed advice from Jill Dyche, partner and co-founder of Baseline Consulting, about what you can do to address the impact of bad data on your company. The report gives you insight into how bad data is impacting your company and what you can do about it. How to identify where the bad data is and quantify its impact, and different approaches to determine the sources and causes of bad data are all offered in this paper.

Abstract: Many business activities require access to real production data, but there are just as many that don’t. Data masking secures enterprise data by eliminating sensitive information, while maintaining data realism and integrity. Many Fortune 500 companies have already integrated data masking technology into their payment card industry (PCI) data security standard (DSS) and other compliance programs—and so can you.

Abstract: The MMOG was a self-assessment process developed by the Automotive Industry Action Group (AIAG) to provide consistent methodology for evaluating supplier performance, identifying weaknesses, and focusing improvement efforts. The Global MMOG/LE incorporates the Odette Logistics Evaluation (OLE), developed by AIAG’s European counterpart, to provide a single global standard for self-assessment. The MMOG is a proven tool for supplier development and provides a recognized industry standard for suppliers who have been asked by customers to complete a self-assessment. Although it was developed by the automotive industry, QAD believes it can be a helpful benchmark tool for customers across many verticals.

Abstract: There is a great deal of confusion over the meaning of data warehousing. Simply defined, a data warehouse is a place for data, whereas data warehousing describes the process of defining, populating, and using a data warehouse. Creating, populating, and querying a data warehouse typically carries an extremely high price tag, but the return on investment can be substantial. Over 95% of the Fortune 1000 have a data warehouse initiative underway in some form.

Abstract: Data auditing is a form of data protection involving detailed monitoring of how stored enterprise data is accessed, and by whom. Data auditing can help companies capture activities that impact critical data assets, build a non-repudiable audit trail, and establish data forensics over time. Learn what you should look for in a data auditing solution—and use our checklist of product requirements to make the right decision.

Abstract: Rising data volume is not the only reason companies are concerned with issues of data integration and data quality. The growing numbers of disparate systems that produce and distribute data add to the complexity. But in many companies, data quality management has not kept pace with the growth of data integration projects, and its use is immature. Find out how moving toward a single data services architecture can help.

Abstract: Companies are fighting a constant battle to integrate business data and content while managing data quality. Data quality serves as the foundation for business intelligence (BI), enterprise resource planning (ERP), and customer relationship management (CRM) projects. Learn more about software that unifies leading data quality and integration solutions—helping your organization to move, transform, and improve its data.

Abstract: Electronic product code information services (EPCIS) is a standard mechanism for inter-company collaboration and data sharing, which can enable health care partners to deploy solutions that meet short-term mandates driven by patient safety, as well as lay the foundation for long-term business value. Learn more about the impact of EPCIS in a study concerning data management and data sharing in the health care supply chain.

Abstract: Oracle Database 11g is a database platform for data warehousing and business intelligence (BI) that includes integrated analytics, and embedded integration and data-quality. Get an overview of Oracle Database 11g’s capabilities for data warehousing, and learn how Oracle-based BI and data warehouse systems can integrate information, perform fast queries, scale to very large data volumes, and analyze any data.

Abstract: Once a revolutionary concept, data warehouses are now the status quo—enabling IT professionals to manage and report on data originating from diverse sources. But where does log data fit in? Historically, log data was reported on through slow legacy applications. But with today’s log data warehouse solutions, data is centralized, allowing users to analyze and report on it with unparalleled speed and efficiency.

Abstract: To derive maximum value from your data, you need a strong data governance program that helps develop and manage data as a strategic business asset. The success of a data governance program thus hinges upon a robust data integration technology infrastructure. Developing the right technology infrastructure is critical to your ability to automate, manage, and scale your data governance program.